prosody_gtsc_phi-3-mini-energy
Ground truth text with prosody encoding residual cross attention multi-label DAC
Model description
Prosody encoder: 2 layer transformer encoder with initial dense projection
Backbone: Phi 3 mini
Pooling: Self attention
Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween
Training and evaluation data
Trained on ground truth.
Evaluated on ground truth (GT) and normalized Whisper small transcripts (E2E).
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Dataset used to train Masioki/prosody_gtsc_phi-3-mini-energy
Evaluation results
- F1 macro E2E on asapp/slue-phase-2self-reported67.270
- F1 macro GT on asapp/slue-phase-2self-reported72.730